FR2907623A1 - DIGITAL RIGHTS MANAGEMENT BASED ON MODIFICATIONS TO THE ENVIRONMENT OF A DEVICE. - Google Patents

Abstract

Some embodiments provide a method for determining whether a subscription for the use of digital content on a user device has expired. The method compares a first conflict-free image of a first set of entities in the user device with a second conflict-free image for a second set of entities in the user device at a second time. On the basis of the comparison, the method determines whether or not the subscription has expired.

Description

FIELD OF THE INVENTION The present invention relates to systems for

  digital rights management and in particular determining the validity of a subscription for the use of digital content.  BACKGROUND OF THE INVENTION The protection of digital content transferred between computers is nowadays of paramount importance to many businesses.  Companies are trying to secure this protection by implementing some form of digital rights management (DRM) processing.  The term DRM refers to a range of techniques used to control access to copyrighted content and to apply the terms and conditions on which digital content is made available to users.  The digital contents may be software, music, videos, news, games, images, or any other content distributed in digital form.  The software portions for implementing the DRM method may run on a reliable or content provider controlled server while other portions of the software may be loaded into the user computer either separately or as part of the a platform that allows the user to download and use the content.  Many models have been designed to distribute digital content.  In certain cases, certain content may be obtained free of charge or be the subject of a free license acquisition.  In these cases, DRM processing simply enforces the distribution of content to authorized computers without any financial objective being taken into account.  In other cases, a pay-per-view model is used where a user pays a fee at the session to download particular content.  Depending on the terms of use, the user is granted limited use, use for a certain period of time, or use for a number of downloads.  In still other cases, a subscription model is used when the user is required to pay a periodic subscription fee.  In these cases, as long as the subscription is up-to-date, the user can download an agreed (sometimes unlimited) number of content files.  The user can also use the contents (for example, listen to a downloaded music track, watch a downloaded movie, play a downloaded game, etc. ) as long as the subscription is up to date.  Therefore, in order to protect the rights of the content provider, DRM processing must be able to determine the validity of a subscription when a user wants to use the content.  As long as the user is online, the DRM process can check the validity of the subscription by comparing the time and expiration date of the subscription with the time and date obtained from a trusted source. .  However, once the user goes offline, the task of ensuring the validity of the subscription becomes more difficult.  When the user is in offline mode, the DRM method must rely on the usually unreliable source indicating the time on the user's device.  In many operating environments, such as Apple's Mac OS X or Microsoft's Windows, a user can easily change the system clock of the user device.  For example, in order to defeat a DRM system that relies solely on the user device's system clock, the user can purchase a subscription for a limited time, download content, and continue to use the downloaded content offline. changing the system clock to a date and time when the subscription is still valid.  Thus, there is still a need in the art to determine whether or not a user's subscription is still valid without relying only on the clock of the user device, particularly in environments where the user is not always connected to a DRM application device.  SUMMARY OF THE INVENTION Some embodiments provide a method for determining whether a subscription for the use of digital content on a user device has expired or not.  The method compares a first conflict free image of a first set of entities in the user device with a second conflict free image for a second set of entities in the user device at a second set of entities. second moment.  On the basis of the comparison, the method determines whether or not the subscription has expired.  In some embodiments, each conflict free image includes a population of slow growing entities in the user device, a population of fast growing entities in the user device, and a population of growth entities. average in the user device.  In other embodiments, each conflict free image contains 15 different types of populations and / or a larger or smaller number of population types.  In some embodiments, comparing the conflicting element free images includes the step of comparing a set of attributes of a set of entities in the user device with a set of acceptable functional parameters.  In some embodiments, the comparison comprises the steps of calculating and comparing calculated statistics for a set of attributes of a set of entities in the user device.  Some embodiments at least partially calculate the statistics at the time of generation of conflicting free images and / or at the time of comparison of conflict free images.  Some embodiments provide a Digital Rights Management (DRM) method which compares the information for a first set of attributes of the user device at a first moment with information of a second set of attributes. at a second moment.  On the basis of the comparison, the method determines whether the environment of the user device (eg, clock, files, directories, etc.).  of the user device) was or was not manipulated to bypass a DRM protection in order to access a set of contents on the user device.  Some embodiments provide a method of distributing digital contents.  The method dispatches content on a subscription basis and uses an evolution algorithm to determine whether or not a subscription for the use of digital content has expired.  The method determines whether or not a population of entities related to a user device has evolved relative to a previously sampled population.  In some embodiments, the method selects the entities and their attributes from a usual population based on their abilities, and modifies the selected entities to form a new population to evaluate an evolution of the next generation of said entities. .  BRIEF DESCRIPTION OF THE DRAWINGS For purposes of explanation, several embodiments are set forth in the following Figures.  Figure 1 illustrates a digital content delivery system of some embodiments.  Figure 2 illustrates a subscription renewal processing of some embodiments.  Figure 3 illustrates a subscription validity check processing with life cycle analysis in some embodiments.  Figure 4 conceptually illustrates an image free of conflicting elements that is a snapshot of several file populations.  Figure 5 illustrates examples of two images free of conflicting elements generated at two different times.  Figure 6 illustrates a conflict-free image construction processing during natural evolution testing in some embodiments.  Figure 7 illustrates a treatment used in some embodiments to determine whether the natural evolution test is required.  Figure 8 illustrates various tests performed during the natural evolution test in some embodiments.  Figure 9 conceptually illustrates a computer system with which certain embodiments are implemented.  DETAILED DESCRIPTION OF THE INVENTION In the following description, many details are set forth for purposes of explanation.  However, one skilled in the art will recognize that the invention can be practiced without using these specific details.  In other cases, well-known structures and devices are shown as schematic diagrams so as not to confuse the description of the invention with unnecessary detail.  Some embodiments provide a method for determining whether or not a subscription for using digital content on a user device has expired.  The method compares a first conflict free image of a first set of entities in the user device with a second conflict free image for a second set of entities in the user device at a second time. .  On the basis of the comparison, the method determines whether or not the subscription has expired.  In some embodiments, each conflict free image includes a population of slow growing entities in the user device, a population of fast growing entities in the user device, and a population of entities at a time. average growth in the user device.  In other embodiments, each conflict-free image has different types of populations and a larger or smaller number of population types.  In some embodiments, comparing the conflict-free image includes the step of comparing a set of attributes of a set of features in the user device with a set of acceptable functional parameters.  In some embodiments, the comparison comprises the steps of calculating and comparing calculated statistics for a set of attributes of a set of entities in the user device.  Some embodiments at least partially calculate the statistics at the time of generation of conflicting free images and / or at the time of comparison of conflicting images.  Some embodiments provide a Digital Rights Management (DRM) method that compares information for a first set of attributes of the user device at a first moment with information of a second set of attributes at a first time. second moment.  On the basis of the comparison, the method determines whether the environment of the user device (eg, clock, files, directories, etc.).  of the user device) has been manipulated to bypass a DRM protection in order to access a set of contents on the user device.  Some embodiments provide a method of distributing digital contents.  The method dispatches content based on a subscription and uses an evolution algorithm to determine whether or not a subscription for the use of digital content has expired.  The method determines whether or not a population of entities related to a user device has evolved relative to a previously sampled population.  In some embodiments, the method selects the features 10 and their attributes from a current population based on their suitability, and modifies the selected entities to form a new population to evaluate a next-generation evolution of said populations. entities.  Several more detailed embodiments of the invention are described in the sections below.  Section I provides an overview of the content distribution system of some embodiments.  This discussion is followed by a discussion of a subscription model used by some embodiments in Section II.  Next, section III describes a life cycle analysis that some embodiments use to test the validity of a user subscription.  Finally, section IV describes a computer system used by some embodiments of this invention.  1.  OVERVIEW Figure 1 illustrates a content delivery system 100 of some embodiments.  This content distribution system distributes content in a manner that protects the digital rights (i.e., which guarantees legal use) of the contents.  As shown in FIG. 1, the content distribution system 100 includes a set of DRM servers 110 and a content receiving user device 115.  Although only one user device 115 is shown in Figure 1, the content distribution system 100 can be used by many user devices.  Figure 1 illustrates the user device 115 in the form of a laptop.  However, the user device 115 may be a desktop computer, a personal digital assistant (PDA), an audio player, a video player, or any other user device capable of receiving the online content.  The user device 115 connects to the set of DRM servers 110 through a computer network 120, such as an enterprise LAN, an extended network, a network of networks (for example, Internet), etc.  Through the network connection, the user device 115 communicates with the set of DRM servers 110 to purchase, license, update, or otherwise obtain contents in certain embodiments.  Therefore, although in some embodiments the DRM server set 110 sells or licenses user devices for content, this set, in other embodiments, does not sell or license for the 35 contents.  For example, in some embodiments, the set of DRM servers 110 simply applies the distribution of content to authorized user devices without any financial purpose.  In some embodiments, the set of DRM servers 110 includes a content capture server that makes the encrypted contents available to a user device 115 through the network 120, after another server in the set of DRM servers 110 has determined that the user device 115 can obtain contents.  In some embodiments, the system 100 uses multiple capture servers to cache content at various locations on the network, to improve the speed and efficiency of downloading content from one end to the other. other of the network.  In some embodiments, the downloaded contents may be copied from the user device 115 to one or more other user devices.  For example, Figure 1 illustrates the other user device in the form of a portable media player 125.  The portable media player may be any device capable of using the downloaded contents.  For example, the portable player could be a music player, a video player, a game console, a camera, etc.  In some embodiments, the portable media player may be connected to the user device 115 via a connection such as USB (Universal Serial 2907623), IEEE 1394, other technical connection.  Many distribute infrared, wireless, or any known in the models were designed for digital content.  In some cases, some content may be obtained free of charge or their license may be acquired free of charge.  In these examples, the DRM server 110 simply applies the distribution of content to authorized computers without any financial objective being taken into account.  In other cases, a pay-per-view template is used where a user pays a fee in session 15 for downloading particular content.  Depending on the terms of use, the user may be granted limited use, use for a certain period of time, or use for a number of downloads.  In still other cases, a subscription model is used when the user is required to pay a periodic subscription fee.  In these cases, as long as the subscription is up to date, the user can download an agreed (sometimes unlimited) number of content files.  The user can also use the contents (for example, listening to a downloaded music track, watching a downloaded movie, playing a downloaded game, etc. ) as long as the subscription is up to date.  Therefore, in order to protect the rights of the content provider, the DRM processing must be able to determine the validity of the subscription when a user wishes to use the contents.  As long as the user is online, the set of DRM servers 110 of some embodiments may control the validity of the subscription by comparing the time and / or expiration date of the subscription with the time and / or date obtained from a reliable source.  However, once the user goes offline, the task of ensuring the validity of the subscription is based on the life cycle analysis of some embodiments.  Some embodiments rely on life cycle analysis to determine the validity of a user subscription even when the user device 115 is connected to the set of DRM servers 110 directly or through a server. other user device.  II.  SUBSCRIPTION MODEL 20 A.  Renewal of subscription As described above, a subscription model is used in some content distribution systems.  In order for the owner of the content to be fairly compensated, a processing intended to guarantee the validity of the subscription is required.  Figure 2 conceptually illustrates a subscription renewal process of some embodiments.  As shown in Figure 2, once the user has entered the offline mode (at step 205), the process determines (in step 210) whether or not the user has a subscription and if his subscription has expired or not.  If the user has a valid subscription, processing proceeds to step 220 which is described below.  On the other hand, if the user does not have a valid subscription, the processing guides the user (at step 215) to purchase or renew a subscription for a certain period of time.  Then (in step 220), the process updates the user's subscription information on the user device 115.  At this point, the subscription information on the user device 115 is synchronized with the information on the DRM server 110.  As long as the user has a valid subscription, the user can download (in step 225) the agreed number of contents in the user device 115.  While the user is online, some embodiments verify the validity of the subscription by means of a reliable source indicating the time.  Once the user has switched to offline mode (in step 230), however, the DRM executing on the user device uses life cycle analysis of certain embodiments to ensure that the subscription for the use of digital content has not expired.  B.  Subscription validity check with life cycle analysis In order to determine whether or not the time indicated by the user device has been falsified or whether the user environment has been tampered with maliciously, some embodiments implement a technique for performing life cycle analysis on the user device.  The life cycle analysis can be performed on both a user device (eg, the user device 115) that connects directly to the set of DRM servers via the network as on a user device. (For example, the user device 125) that connects to the set of DRM servers through other user devices.  The life cycle analysis is performed on a user device by the user device.  To accomplish the lifecycle analysis, multiple groups of files, folders, directories, volumes, or other user device entities with known or predictable behaviors are selected and analyzed.  Periodically, a snapshot of certain attributes of these entities (for example, the date of the last access of a file, the date of creation of a file, the size of a folder, etc.). ) is taken and saved.  Some embodiments calculate the statistics for some of the attributes and save these calculations.  The evolution of these populations, for example changes in the attributes as well as the absence or presence of certain files or directories, is then analyzed to determine whether the observed changes between two snapshots are related to the elapsed time indicated by the system clock of the user device.  Figure 3 conceptually illustrates the subscription validity check processing 300 of certain embodiments.  In step 305, the processing determines that the user wishes to use downloaded content based on the subscription template.  For example, user 5 may wish to listen to a music track, watch a movie, read an article, watch an image, play a game, etc.  In step 310, the user processing not expired.  uses the clock of the device to control whether the subscription has or If the subscription has expired on the base 10 of the user user's clock, it is requested (in step 315) to switch to online mode and renew the subscription.  Otherwise, the process uses (in step 320) a natural evolution test to determine if the user device's clock may have been modified or if the environment of the user device has been manipulated.  The details of the natural evolution test are described in the following sections.  If the evolution test determines (in step 320) that the environment of the user device has been manipulated or the clock of the user device is unreliable (and therefore the subscription may have expired ), the user is requested (in step 325) to go online and synchronize the subscription with the set of DRM servers 110.  Otherwise, if in step 330 the evolution test determines a normal evolution, the user is allowed to use the downloaded contents that were obtained through the user's subscription.  In some embodiments, the evolution test is performed at the same time that the user is using the contents.  In these embodiments (not shown), the user is required to synchronize after the current content is used by the user.  In other embodiments, the user is prevented from using the downloaded contents until the user has been able to synchronize with the DRM server 110.  In the following discussions, the life cycle analysis is described by way of several examples.  However, one skilled in the art would appreciate that the invention can be practiced without the use of these specific details.  For example, life cycle analysis can be performed on any user device (such as personal computers, music players, video players, personal digital assistants, cameras, etc.). ) which is capable of executing software for performing the life cycle analysis and which includes a set of files or other entities whose attributes may be acquired for the purpose of performing the analysis.  Likewise, the life cycle analysis is not limited to the attributes and file categories shown in the examples and can be practiced using other attributes and other user device entities to perform the task. 'analysis.  III.  NATURAL EVOLUTION TEST In some embodiments, the life cycle analysis involves a natural evolution test.  The natural evolution test is performed by comparing the current snapshot of the attributes of a population with a snapshot preceding population attributes to determine whether the changes are acceptable.  The natural evolution test also ensures that the rest of the user environment has not been modified to mislead the life cycle analysis.  Based on the comparison of the current snapshot with the previous snapshot, the natural evolution test determines whether or not the environment of the user device has been manipulated to bypass DRM protection in order to access content. on the user device.  As described below, populations and their attributes to monitor are selected so that neither snapshot can be the same.  Thus, each snapshot generates an image free of conflicting elements (CFI) for the selected populations.  Some embodiments save certain attributes of the selected populations in the CFI image.  Some embodiments calculate statistics of certain attributes of different populations and save the statistics in the CFI image.  In some embodiments, the CFI image contains a combination of individual attributes of certain population members (e.g., certain file attributes, directories, volumes, etc.).  individuals with known behavior) as well as population portion statistics as a whole (for example, a total average size of all files in a certain population).  In some embodiments, the CFI image also contains name and / or location information to identify each population and its members.  AT.  The Exempt Image of Elements in Conflict (CFI) 1.  Selection of populations and attributes Some embodiments take snapshots of certain populations of files, folders, directories, volumes, or any other entities of any hardware or software of the user device having known or predictable behavior and save their attributes (or statistics of these attributes) in an image free of conflicting elements (CFI).  In some embodiments, each snapshot creates a separate CFI image.  The selection of these populations (to generate the CFI image) depends on the particular user device.  Some embodiments perform selection based on the environment of the user device, for example, the particular operating system that executes on the user device, applications running on the user device, and the hardware configuration of the user device.  During the software development phase that implements life cycle analysis, the structure, type, attributes, and behaviors of each population are carefully analyzed to determine a set of entities (such as files, folders, directories, volumes, hardware profiles, etc. ) with predictable behavior.  Heuristic and trial and error methods can be used, as well as a study of the description documents and system development guides to identify a set of device entities and their specific attributes to be used in the cycle analysis. of life.  Some embodiments identify different groups of entities with similar behavior and create a population from these groups.  For example, some embodiments identify slow, medium, and fast moving entities in a user device and assign them to different populations.  Populations in the user device are analogous to human populations.  Some human populations, for example, tribes living in remote areas, change very slowly.  In a certain period of time, very few people come and go in these tribes and these tribes have very few births and 25 deaths.  On the other hand, in large metropolitan areas, people come and go at any time and the number of births and deaths is quite significant.  There are, however, other cities or towns with moderate evolutions.  Therefore, some embodiments analyze three types of population to create a snapshot of slow-moving, moderately populated, and rapidly changing populations.  A person skilled in the art will recognize that the populations of entities in a user device need not be limited to the three populations mentioned in the example above, since additional or lesser levels of granularity can be used. .  Some of the examples given below, however, use the three categories of slow, medium and fast changing populations for the sake of simplicity.  The natural evolution test of some embodiments implements a genetic algorithm that determines whether a population of entities related to the user device (i.e., entities in the user device or entities in the user device). the environment of the user device, for example, entities in an enterprise LAN connected to the user device) has or has not evolved with respect to a previously sampled population.  Genetic algorithms constitute a particular class of evolutionary algorithms that utilize Darwinian evolutionary theory-inspired techniques such as inheritance, mutation, natural selection, and genetic mixing.  The natural evolution test of these embodiments begins with a certain population.  Evolution starts from the first set of entities in the population and evolves from generation to generation.  In each generation, the entire population is evaluated to determine whether the evolution reflects the elapsed time indicated by the user device.  In these embodiments, entities and their attributes are selected from the current population on the basis of their modified, ie, mutated or genetic mixing, abilities in order to train a new population to assess the evolution of the next generation.  Figure 4 conceptually illustrates a CFI 400 image that includes a snapshot of three types of populations 405 to 415 in a user device.  Compared with human tribes, these populations may be slow-moving, rapidly changing, or somewhere in between.  For example, in some operating systems, system files and directories are an important population.  Although the population may have many members, the population is static with the exception of dates of last access by members of the population.  The user can frequently use system entities but can rarely upgrade or update them.  On the other hand, files and directories 25 users can constitute a large population with rapid evolution.  A user can daily browse the Internet and add cookie files and download files.  The user can edit, edit, create, and delete files and directories in the user area.  Another example of a population may be a group of applications.  In some embodiments, the application files may constitute a small population that is primarily static.  One skilled in the art will recognize that system files and directories, users and applications are just examples of entities having known or predictable behavior.  Once different populations and their relevant attributes are determined, heuristic and trial and error methods will be used to determine a correct frequency with which snapshots of populations and their attributes will be taken.  Snapshots will be taken at this frequency to identify expected changes in a user device.  For example, Figure 5 illustrates an example of two snapshots 505 and 510 of a system taken approximately two days apart.  In this figure, the numbers are hypothetical and are used only as examples.  As shown in Figure 5, the file "WindowUpdate. log "located in the" C: / WINDOWS "folder is on both CFI (snapshot) images 505 and 510.  The two snapshots 515 and 520 of this file show that the size of this file has increased from 1,650 kilobytes (KB) to 1,860 KB in the time interval in which both snapshots were taken.  As shown, the "modification date" and "access date" attributes have also changed while the "creation date" attribute has not changed.  Whether the two snapshots of this file will successfully pass the evolution test depends on how the heuristics and evolution methods had predicted the evolution of this file in the interval of time. time between the two snapshots.  Based on the heuristic, trial and error information in various system documents and manufacturers, the evolution test would have a predetermined change threshold for different attributes of that file.  For example, assume that based on the experiments the life cycle analysis determined that the size of this specific file (which is a system log file) will change by at least 10 kilobytes (KB) per day and it will be accessed and modified at least once a day.  The creation date of the file will not change, however.  In these cases, this specific file will successfully pass the evolution test.  Nothing unexpected about this file is detected during the period of approximately two days indicated by the clock of the user device.  On the other hand, if the life cycle analysis had determined that the file size should change by about 150 KB per day, the file will not pass the evolution test successfully.  The Acrobat file. exe shown in Figure 5 is another sample file for which we take two snapshots 525 and 530.  As shown, this file is located in the "C: \ ProgramFiles \ Adobe \ Acrobat 5" folder. 0 ".  Suppose that it is an application file and that the evolution test does not provide for a change except for the "Access Date" attribute.  However, as shown in the example, the "Access Date" attribute of the snapshot taken on 03/04/2006 at 20:56 h shows a date of last access to 05/04/2006. at 17:48 h.  This will indicate to the evolution test that the clock of the user device which indicates that the last snapshot was taken on 03/04/2006 at 20:56 h may not have that shown in Figure 3, it will 10 go into subscription mode.  As described embodiments, contain statistics for all or part of different populations.  The conceptual example shown in Figure 5 consists of the two snapshots 535 and 540 taken for a group of folders that contain cookie files for different users.  In some operating systems, multiple users may have access to a particular user device and each user may be assigned his own area of the memory space (for example, a hard disk) to receive witness files uploaded to a particular user device. the hard drive while the user browses the Internet.  In the example shown on snapshots 535 and 540, the CFI image relates to statistics on many or all of the hard drive folders that contain user cookie files.  In other embodiments, not shown, individual files contained in cookie files for online requests and been correct.  step 325 As on the user to synchronize sound above, in some the CFI image can also 2907623 26 individual users can be monitored.  As shown, the two snapshots 535 and 540 show that the total size of this population has increased from 147,618 bytes to 168,618 bytes between the two snapshots.  Similarly, the number of objects in the population has increased from 99 to 120.  The example also shows that the oldest creation date, the date of the last modification, and the date of the last access of the population are also monitored.  As shown, the attributes "Last Modified Date" and "Last Access Date" have changed between snapshots 15 while the oldest "Date Created" attribute has not changed.  Whether or not the two snapshots 535 and 540 successfully pass the evolution test depends on how the heuristic life cycle analysis method predicted the evolution of this population over the two days elapsed time. an hour and 24 minutes that the clock of the user device shows between the two snapshots.  As shown, the first snapshot was taken on 01/04/2006 at 19:32 h and the second snapshot was taken on 03/04/2006 at 20:56 h.  Based on the heuristic and trial and error information in various system and manufacturer documents, the evolution test would have a predetermined change threshold for different attributes of that population.  For example, suppose that on the basis of the experiments, life cycle analysis determines that the size of a specific population that contains witness files would change by at least 5KB per day but not more than 20KB. per day.  Similarly, suppose that life cycle analysis predicts that there will be at least 5 witness files per day, will be modified and / or access to the

  population at least once a day and the earliest creation date will not change. In these hypotheses, the two snapshots 535 and 540 would successfully pass the evolution test. Nothing unexpected is detected in this population during the period indicated by the clock of the user device. On the other hand, if the heuristic life-cycle analysis method 15 had determined that the population size would change by at least 15 KB per day, or that there would be at least 20 objects per day added to the population, the population would not have successfully passed the test of evolution.

The above examples were given using a Microsoft Windows XP Professional operating system. One skilled in the art would recognize that other operating systems may have a different file and folder structure, a different nomenclature for their file structure, may have other attributes that can be monitored, and so on. In addition, snapshots can be taken periodically and / or on the basis of certain events (for example, as soon as a user tries to access a downloaded content or as soon as a user has accessed a predetermined number of contents downloaded). Likewise, the thresholds used by the evolution test may have a granularity different from a day or even a certain period of time. The thresholds may be based on a frequency of access to downloaded content or any other acceptable criteria determined by trial and error, heuristic methods, etc. 2. Construction of the CFI Image Figure 6 illustrates a process 600 for constructing an image free of conflicting elements in some embodiments. During the development phase of the life cycle analysis software, some embodiments identify multiple populations for each type of user device environment. In runtime, the life cycle analysis process selects the populations that are identified for the particular type of user device. In some embodiments, the population members are selected before each snapshot is taken. In other embodiments, the members of the populations are selected less frequently. As shown, the process initially identifies (in step 605) a rapidly changing population on the user device. The process obtains (at step 610) information from the user device about the attributes of the population members. As indicated above, examples of certain attributes are the size, creation date, date of last modification, date of last access, absence or presence of certain files or directories, etc. . In some embodiments, the selected attributes of the environment in which the user device operates are also included in the CFI image. One skilled in the art would find that other attributes can also be used without departing from the teaching of the invention. The processing then saves (in step 610) the information in the CFI image. The saved information can be the current values of the selected attributes of particular files or folders or other entities. The saved information may also be in the form of statistics computed for some members of the population. For example, the statistics may include an average deviation, a maximum deviation, a minimum deviation, an average absolute deviation, a standard deviation, or any other statistic calculated for some or all members of the population. Similarly, the process identifies (in step 615) a population of medium-evolution device entities. In step 620, the processing obtains information from the user device on the attributes of members of the population and adds them to the CFI image. Similarly, the process identifies (in step 625) a population of slow moving device entities. The process then obtains information (in step 630) from the user device on the population member attributes and adds the information to the CFI image. Although the treatment 600 is represented as identifying and collecting information for three types of population, the evolution test is not limited to three populations nor limited to the only population selection on the basis of the speed of their evolution. Thus, the treatment 600 can identify multiple populations based on criteria determined during development of the evolution phase for the particular user device and can use these populations to generate the CFI image. B. Determination of the Knowing Whether the Evolution Test is Required In some embodiments, the evolution test is not performed each time a user accesses downloaded content. Different embodiments use different criteria to determine whether or not the evolution test needs to be performed. For example, some embodiments may perform the evolution test once on multiple user accesses to any content, once on multiple user accesses to a particular content, randomly, or according to any other criteria determined during the course of time. development of the evolution test. Some embodiments may use a combination of several criteria to determine whether or not the evolution test should be performed. Also, as described below, once the evolution test has determined that the user needs to synchronize the subscription, some embodiments establish an internal flag that prevents the user from synchronizing the subscription. access to the contents without any further trial of any change being made until the subscription is synchronized.

Figure 7 illustrates a processing 700 that some embodiments use to determine whether or not the evolution test should be performed. The treatment is activated when it may be necessary to perform the evolution test. For example, reference is made to Figure 3 wherein the processing 700 of some embodiments may be enabled before the operation 320 is performed. Thus, in these embodiments, operation 320 (and operation 325 if necessary) will only be performed if processing 700 determines that the evolution test must be performed. As shown in Figure 7, the treatment determines (at step 705) whether or not the criteria for completing the evolution test have been met. For example, if in a particular embodiment, the evolution test is performed every user access to the content that requires a subscription, the process checks whether the user has already accessed the content at least ten times. . If the criteria are not satisfied, the processing at step 710 updates the criteria, for example, increments an internal counter, and exits. Otherwise, the processing constructs (at step 707) a CFI image by taking a snapshot of the populations that are identified for the natural evolution test.

Next, the active treatment (at step 715) the natural evolution test. Details of the evolution test are not shown here but are represented elsewhere in this specification.

In step 720, the treatment checks whether or not the natural evolution test indicates that the evolution is acceptable. If the answer is yes, the process resets (at step 725) the criteria for performing the evolution test.

The treatment then comes out. Otherwise, the processing establishes (in step 730) an internal flag to prevent the user from using content that requires a subscription to access it. This internal flag is reset once the user has synchronized the subscription. The treatment then comes out. C. Type of Tests Performed Some embodiments perform several tests during the evolution test. Some embodiments perform individual as well as global tests. Individual tests include the step of controlling the evolution of the attributes of the individual entities (files, folders, volumes, etc.) that have predictable behavior. If any (or some) of these tests fail, the evolutionary attempt would fail as a whole. The global tests include the step of controlling the statistics for many entities in a population. An example of a global test has been described above with reference to snapshots 535 and 540 in Figure 5. Several global tests can be performed on different groups of entities in a population. If any (or some) of these tests fail, the evolutionary attempt would fail as a whole. Some embodiments perform a global evolution test for a set of entities by comparing a weighted calculation (e.g., a weighted sum) of changes in a set of attributes of the set of features with a set of features. threshold for determining whether or not the set of entities passes the global evolution test. For example, the following equation (A) represents a weighted sum of changes in the set of n attributes (file size, 15 directories, etc.) of a set of entities. LZ = i * AZ (A) where n is the attribute name of the set of 20 entities, Di is the quantity of the ith modified attribute and Wi is the weighting factor given to the modification of the ith attribute. This weighted sum is then compared to a predetermined threshold. For some global tests, two thresholds can be used to compare the changes to a range with an acceptable lower and upper threshold. Figure 8 conceptually illustrates a treatment 800 that performs several different tests during an evolution test episode. Although the example of Figure 8 is shown with three populations, the evolution test can be performed with any number of populations. Likewise, to better illustrate the concept of different tests, FIG. 8 only shows the operations that determine whether or not each test group was successful. Other details of the evolutionary tests, such as the operations intended to perform the tests, are not shown for purposes of simplification. In step 805, the treatment checks whether or not the individual tests for the fast-growing population have been passed successfully. If these tests fail, the process proceeds to step 840 to indicate that the test failed. The treatment then proceeds to step 845 which is further described below. Otherwise, the control treatment (at step 810) whether or not the overall trials for fast-growing populations have been successfully completed. If these tests fail, the process proceeds to step 840 to indicate that the evolution test has failed. Otherwise, the control treatment (at step 815) whether or not all the individual trials for the average population have been passed successfully. If these tests fail, the process proceeds to step 840 to indicate that the evolution test has failed. Otherwise, the control treatment (at step 820) whether or not the overall trials for the medium-lived populations have been passed successfully. If these attempts fail, the process proceeds to step 840 to indicate that the evolution test failed. Otherwise, at step 825, the treatment checks whether all the individual trials for the slow-growing population have been successful or not. If these tests fail, processing proceeds to step 840 to indicate that the evolution test failed. Otherwise, the control treatment (at step 830) whether or not the 5 global trials for slow-growing populations have been successfully completed. If these tests fail, processing proceeds to step 840 to indicate that the evolution test failed.

Finally, if all the tests have been successfully passed, the process proceeds to step 835 to indicate that the evolution test has been successfully completed. The save processing (at step 845) the current CFI image to be used as a snapshot when the evolution test is then performed. The treatment then comes out. Although the example in Figure 8 shows that both individual and global tests are performed for each population, in some embodiments, for some populations, the assays may be limited to either individual trials or global tests. IV. COMPUTER SYSTEM Figure 9 conceptually illustrates a computer system with which certain embodiments of the invention are implemented. The computer system 900 includes a bus 905, a processor 910, a system memory 915, a read-only memory 920, a permanent storage device 925, input devices 930, and output devices 935. The bus 905 collectively represents all system, peripheral, and circuit board buses that support communication among the internal devices of the computer system 900. For example, the bus 905 communicatively connects the processor 910 with the read-only memory 920, the system memory 915, and the permanent storage device 925. From these various memory units, the processor 910 retrieves instructions to execute and data to be processed in order to execute the treatments of the invention. The ROM 920 stores static data and instructions which the processor 910 and other modules of the computer system need. The permanent storage device 925, on the other hand, is a dual port memory device. This device is a non-volatile memory unit that stores instructions and data even when the computer system 900 is de-energized. Some embodiments of the invention utilize a mass storage device (such as a magnetic or optical device and its corresponding disk drive) as a permanent storage device 925. Other embodiments use a storage device 25 (such as a floppy disk or zip disk, and its corresponding disk drive) as a permanent storage device. Like the permanent storage device 925, the system memory 915 is a dual access memory device. However, unlike the storage device 925, the system memory is a volatile dual access memory, such as a random access memory. The system memory stores some of the instructions and data that the processor needs at run time. The instructions and / or data necessary to perform processing of certain embodiments are stored in system memory 915, permanent storage device 925, ROM 920, or any combination of these three elements. For example, the various memory units may contain instructions for processing multimedia objects according to some embodiments. From these various memory units, processor 910 retrieves the instructions to be executed and data to be processed in order to perform the processing of some embodiments. The bus 905 also connects to the input and output devices 930 and 935. The input devices allow the user to communicate the information and select commands to be sent to the computer system. Input devices 930 include alphanumeric keypads and cursor keys. The output devices 935 display images generated by the computer system. For example, these devices display the integrated circuit design (IC) provisions. Output devices include printers and display devices, such as cathode display (CRL) devices or liquid crystal display (LCD) devices. Finally, as shown in Figure 9, the bus 905 also couples a computer 900 to a network 965 via a network adapter (not shown). In this way, the computer can be part of a computer network (such as a local area network ("LAN"), a wide area network ("WAN"), or an intranet) or a network. network of networks (such as the Internet). Any or all of the components of computer system 900 may be used in conjunction with the invention. However, one skilled in the art will recognize that any other system configurations may also be used in conjunction with the invention. Although the invention has been described with reference to many specific details, one skilled in the art will appreciate that the invention may be embodied in other specific forms without departing from the spirit of the invention. . For example, life cycle analysis can be performed on any user device (such as personal computers, music players, video players, personal digital assistants, cameras, etc.) capable of execute software to complete the life cycle analysis and having a set of files or other entities whose attributes can be acquired to perform the test. Likewise, life cycle analysis is not limited to the attribute and file categories shown in the examples below and can be practiced using other attributes and device entities. user to perform the test. For example, a population of entities in a user device need not be limited to the three populations mentioned in some of the above examples, since additional or lower levels of granularity can be used. In other places, various changes may be made and the equivalents may replace elements described without departing from the true scope of the present invention.

For example, system, user, and application files and directories have been represented as an example of entities having known or predictable behavior. Many entities and populations can be identified for use in the evolution test. For example, in some embodiments, the hardware profile of the user device is also used for life cycle analysis. Although in the examples above, user device local entities for life cycle analysis have been used, in some embodiments, entities in the user device environment, including a local area network. (LAN) or a wireless LAN (WLAN) are also taken into account. Similarly, the evolution test can be implemented in user devices running different operating systems.

These operating systems may have a different file and folder structure, a different BOM for their file structure, may have other attributes that can be monitored, and so on. For example, some operating systems may use the terms directories and subdirectories while others use the terms folders and subfolders. Likewise, the computer system shown in Figure 9 is just one example of the many different hardware configurations that a user device may have. For example, a portable media player may have a different hardware configuration than that shown in Figure 9. In addition, snapshots may be taken periodically and / or on the basis of certain events, for example, as soon as a user tries accessing a downloaded content or as soon as a user has accessed a predetermined number of downloaded contents. Similarly, the evolution test may not be performed at fixed time intervals. Instead, the completion of the evolution test can be done on the basis of the frequency of access to the content which requires a subscription or on the basis of any other acceptable criterion determined by test methods and errors, heuristics, etc. Thus, one skilled in the art will appreciate that the invention is not limited to the details previously illustrated, but rather should be defined according to the appended claims.

Claims (14)

  A method comprising the steps of a) identifying a particular device environment in which a plurality of contents will be accessed; b) identifying a set of entities on the particular device environment that have attributes with predictable behaviors; and c) defining a digital rights management (DRM) method to be used during an execution of a device including the particular device environment, the DRM method determining whether the changes in the attributes of each entity are or not as expected.   The method of claim 1, wherein the particular device environment comprises a set of files on the device.   The method of claim 1, wherein the particular device environment comprises a set of directories on the device.   The method of claim 1, wherein the particular device environment comprises operating system software.   The method of claim 1, wherein the particular device environment comprises a network to which the device will connect.   The method of claim 1, wherein the particular device environment comprises a hardware configuration of the device.   The method of claim 1, wherein attributes with predictable behaviors are determined by trial and error.   The method of claim 1, wherein attributes with predictable behaviors are determined using a heuristic method.   The method of claim 1, wherein attributes with predictable behaviors are determined by studying system documents of the particular device environment.   The method of claim 1, wherein the device comprising the particular device environment is a computer.   The method of claim 1, wherein the device comprising the particular device environment is a portable reader.   12. A computer-readable medium storing a program to be executed, the program comprising sets of instructions for a) analyzing attributes of a set of entities on a device at different times when a reliable time source does not exist. is not available; and (b) allow access to digital media when the attributes have changed in the expected thresholds.   The computer-readable medium of claim 12, wherein the program further comprises a set of instructions for requiring the device to synchronize with a subscription to use the digital media over a communicating connection to a computer. a remote computer having a reliable source of time.   The computer-readable medium of claim 12, wherein the program further comprises a set of instructions for estimating time elapsed between different times by analyzing changes in said attributes.